similar to: correlation question

Dear all, The other day I was reading this post [1] that slightly surprised me: "To reject the null of no correlation, an hypothsis test based on the normal distribution. If normality is not the base assumption your working from then p-values, significance tests and conf. intervals dont mean much (the value of the coefficient is not reliable) " (BOB SAMOHYL). To me this implied that in

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,

my programe is below: a=c(1,2,1,1,1); b=c(1,2,3,4,1); c=c(3,4,3,3,3); d=c(1,2,3,5,1); e=c(1,5,3,5,1) data.f=data.frame(a,b,c,d,e) origin.data<-data.f cor.matrix<-cor(origin.data) origin.cor<-cor.matrix m<-0 for(i in 1:(cor.matrix[1]-1)) { for(j in (i+1):(cor.matrix[2])) { if (cor.matrix[i,j]>=0.95) { data.f<-data.f[,-i]; i<-i+1 }

Dear list I would like to compare two measurements of disease severity (M1 and M2), one of the is continuous (M1 ranging from 1 to 10) and the other is ordinal (M2 takes Low, Medium, high and very high). Do you think is ok to use cor() function to test whether the two agree, i.e correlate? I am afraid that if I set M2 to 1,2,3 and 4, the function cor() will take them as continuous and

Hi R users, I generated a square correlation matrix for the dat dataframe below; dat<-data.frame(g1=c(1,0,0,1,1,1,0,0,0), g2=c(0,1,0,1,0,1,1,0,0), g3=c(1,1,0,0,0,1,0,0,0), g4=c(0,1,0,1,1,1,1,1,0)) library("Hmisc") dat.rcorr = rcorr(as.matrix(dat)) dat.r <-round(dat.rcorr$r,2) however, I want to modify this correlation calculation;

Whether the two variables have the same units does not matter. Moreover, even if there were some way of converting cm to kg the correlation would still be the same because the correlation is invariant under unit conversion as it is invariant under multiplication of its arguments by a constant. As for your second question, the correlation estimator is a continuous function of each of the

I tried 2 methods to estimate C.I. of correlation coefficient of variables x and y: > x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1) > y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8) #METHOD 1: Pearson's ********************************************************** > cor.test(x, y, method = "pearson", conf.level = 0.95) Pearson's

Hi, I am just starting to use R for a graduate course, and I like how the correlation matrix at http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=137 I did something similar by copying from the examples(pairs), but it seems that I need to jitter the bottom panel... and I have no idea how to do that, and I mean no idea at all. I'd appreciate any help... Here are the graphs:

?s 17:39 de 25/07/2024, Yuan Chun Ding via R-help escreveu: > Hi R users, > > I generated a square correlation matrix for the dat dataframe below; > dat<-data.frame(g1=c(1,0,0,1,1,1,0,0,0), > g2=c(0,1,0,1,0,1,1,0,0), > g3=c(1,1,0,0,0,1,0,0,0), > g4=c(0,1,0,1,1,1,1,1,0)) > library("Hmisc") > dat.rcorr =

Is there a simple way to run cor.test on for a matrix of correlations? Of course, cor on a data frame produces a correlation matrix, but cor.test will only take two variables at a time. Is there a way to get behavior similar to that of cor with cor.test? I suppose the programming alternative would be to run two for loops with the number of items and cor test embedded accessing the columns of

Hi, I have the following two measurements stored in mat: > print(mat) [,1] [,2] [1,] -14.80976 -265.786 [2,] -14.92417 -54.724 [3,] -13.92087 -58.912 [4,] -9.11503 -115.580 [5,] -17.05970 -278.749 [6,] -25.23313 -219.513 [7,] -19.62465 -497.873 [8,] -13.92087 -659.486 [9,] -14.24629 -131.680 [10,] -20.81758 -604.961 [11,] -15.32194 -18.735 To calculate the ranking

Hi. I am currently trying to run some Spearman correlations, and have encountered two issues. 1) When using cor.test() with a variable that includes ties, I get the "Cannot compute exact p-values with ties" error. I have read that this function now uses an asymptotic formula that allows for ties, so do not understand why I am getting this error. (I am running version 2.4.0.) I

Hello, I used the function cor to calculate the pearson correlation coefficient between variables. However, the resulting values do not correspond to the outcome of my excel-calculations, for which I used the formula Cor(x,y)=Cov(x,y)/(SD(x)*SD(y)) So my question is: How does the function "cor" compute the pearson correlation coefficient? Thank you in advance, Ake Nauta

Hi, I have recently been using the R program and encountered a recurring problem. I have been trying calculate the correlation of a 16 column table. Everytime I type in cor(test), where test is data that I uploaded into R using the read.table function, I get an error: Error in cor(test) : missing observations in cov/cor In addition: Warning message: In cor(test) : NAs introduced by coercion

I'm trying to obtain within-group correlations on a subset of variables. I first selected my variables using the following command: mydata$x<-mydata[c("iq","education","achievement")] I'd like to look at correlations among those variables separately for men and women. My gender variable in mydata is coded 1 (women) and 0 (men). I have successfully used

Hi, How can I make the cor(x, y, method="spearman") call to produce an error when the input to it (x, y) produces an error? Here is a simple example: > a <- c(0, 1, 2) > b <- c(100, 2, 4) ## error: > log(a) [1] -Inf 0.0000000 0.6931472 ## error, as expected: > cor(log(a), log(b), method="pearson") [1] NaN ## not an error any more (not expected): >

Hi, I am using R 1.8.1 on WinXP. I encounter a problem when trying to compute a Spearman correlation under certain conditions (at least I think there is a problem, but maybe this is the normal behavior). > X<-array(0,c(20,2)) > > X[,1]<-c(runif(10),rep(NA,10)) > X[,2]<-c(runif(10),rep(NA,10)) > > Y<-X[1:10,] > >

HI Rui, Thank you for the help! You did not remove a row if zero values exist in both column pair, right? Ding From: Rui Barradas <ruipbarradas at sapo.pt> Sent: Thursday, July 25, 2024 11:15 AM To: Yuan Chun Ding <ycding at coh.org>; r-help at r-project.org Subject: Re: [R] please help generate a square correlation matrix ?s 17:?39 de 25/07/2024, Yuan Chun Ding via R-help

Hello R gurus, I want to calculate the Spearman rho between two ranked lists. I am getting results with cor.test that differ in comparison to my own spearman function: > my.spearman function(l1, l2) { if(length(l1) != length(l2)) stop("lists must have same length") r1 <- rank(l1) r2 <- rank(l2) dsq <- sapply(r1-r2,function(x) x^2) 1 - ((6 * sum(dsq))

My two matrices are roughly the sizes of m1 and m2. I tried using two apply and cor.test to compute the correlation p.values. More than an hour, and the codes are still running. Please help to make it more efficient. m1 <- matrix(rnorm(100000), ncol=100) m2 <- matrix(rnorm(10000000), ncol=100) cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) { cor.test(x,y)$p.value